If you follow the news at all, you can’t have missed the discussions about the Precision Medicine Initiative, which first came to light in President Obama’s State of the Union speech in January. The initiative is described as “a bold new research effort to revolutionize how we improve health and treat disease”… “a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.” Participants will “contribute diverse sources of data —including medical records; profiles of the patient’s genes, metabolites (chemical makeup), and microorganisms in and on the body; environmental and lifestyle data; patient-generated information; and personal device and sensor data.” If you think this sounds like a lot of data, you are correct. This is most definitely a “big data” project. So what’s required to make it happen? As a foundation, at least three things — lots of data, good data, and one searchable data source. Let’s look at these factors briefly:
- Lots of data – The volume of data needed is so great that the NIH will probably start with existing research populations, like the members of the Framingham study, where there are already decades of physical, pharmacologic, and genetic research extensively documented.
- Good, precise data – If you have ever tackled a big data (or even a medium data) project, you know that one of the keys to valuable results is valuable data. The data needs to be accurate – you have to be able to trust that the data means what it says. But just as critically, the data needs to be of sufficient detail, i.e. sufficiently granular, that it can be used to identify disease states and interventions uniquely.This requirement for data granularity brings ICD-10 into the picture front and center.It’s not hard to imagine that, with ICD-9’s 17 codes for a femur fracture compared to ICD-10’s over 2,600 codes, it would be much easier to find patients that reflect a very small and homogeneous data set to research personalized interventions using ICD-10 coded data. ICD-9’s design from 36 years ago, even with annual updates, simply can’t adequately describe 21st century medicine. That’s just one of the reasons ICD-10 is here to stay.
- One searchable data source to contain the data — derived from interoperability of many systems. It does no good for personalized medicine (or Meaningful Use for that matter) to create and store mountains of data in separate electronic systems that can’t be searched. This, plus the practical needs of data interchange between providers for Meaningful Use, ensures that the interoperability standardization will proceed aggressively.
Certainly, the country has never been in a better position than today to achieve true interoperability between disparate healthcare providers and IT systems. Meaningful Use incentives have moved the industry along and continue to do so. At ONC’s Annual Meeting in Washington, D.C., earlier this month, HHS and ONC announced various new government funding programs to increase the momentum towards interoperability, including $28 million in federal funding in support of health information exchanges that are dealing with interoperability issues. But, in the same meeting, during a panel discussion of all five past and present national coordinators for health IT, concern was expressed that EHR vendors are not yet on the interoperability bandwagon. As David Brailer said, “If you are an EHR vendor with any significant market share, interoperability is not your top priority.” He added that asking vendors to change their business practices and proactively improve interoperability is a challenge. David Blumenthal and Farzad Mostashari agreed — the latter noting that “vendors haven’t been incentivized to make progress on interoperability and data sharing….(this is ) a market failure.” The panel members all agreed that the Obama administration’s push on payment reform could have a major impact on eliminating this problem. ICD-10, Meaningful Use, and the big data initiatives we are discussing aren’t the end goals of Health IT. They are just essential parts of an IT infrastructure that will be used to improve quality and lower the cost of healthcare. With that infrastructure in place, we can better examine how diseases and treatments interact in specific populations, speed the development of new and more precise treatment strategies, and ultimately provide better healthcare. Phoenix Health Systems has worked for years to help mid- to large-sized hospitals implement technology strategies that allow them to participate in the benefits of technology at a reasonable cost. If you’d like to know more, call us….